A steel manufacturing process using a blast furnace is a complex, continuous operation that involves multiple chemical reactions and phase transitions of materials. To better control the blast furnace during the manufacturing process for continuous production of quality metals, it is useful to be able to predict state variables in the future time associated with the production such as the temperature of the hot metal produced by the blast furnace. Generally, prediction algorithms utilize historical data for predicting the future data. However, in steel manufacturing process involving the blast furnace, the state variables such as the hot metal temperature (also referred to as the pig iron temperature) are measured sparsely, for example, once in every few hours at an irregular interval. Sparse, irregular measurement data makes it difficult to be able to accurately predict the future data.
Other examples of continuous manufacturing processes include the aluminum smelting process, in which temperature of aluminum bath is measured once in two days, and the cement manufacturing process measuring fineness of cement particles once in an hour in a grinding station.